To see the validity of assumption A, suppose that there are 10 parties \(i= 1,\ldots , 10\) , each of whom intends to train a binary classifier on MNIST digits to predict if a digit is \(i\)  or not. But, each party \(i\) 's dataset \(D_i\)  only comprises images of digit \(i\)  and hence a restricted subset of the support of \(\mathcal {D}\)  (i.e., imbalanced data). If every party can access images of all 10 digits, then their classifiers
can perform better due to availability of negative examples. In general, imbalanced data in classification problems yield trained ML models that make predictions biased towards the majority classes, which is an issue receiving significant attention [1]}.
